Parallel Processing for Multi-objective Optimization in Dynamic Environments

This paper deals with the use of parallel processing for multi-objective optimization in applications in which the objective functions, the restrictions, and hence also the solutions can change over time. These dynamic optimization problems appear in quite different realworld applications with relevant socio-economic impact. The procedure here presented is based on PSFGA, a parallel evolutionary procedure for multi-objective optimization. It uses a master process that distributes the population among the processors in the system (that evolve their corresponding solutions according to an island model), and collects and adjusts the set of local Pareto fronts found by each processor (this way, the master also allows an implicit communication among islands). Moreover, the procedure exclusively uses nondominated individuals for the selection and variation, and maintains the diversity of the approximation to the Pareto front by using a strategy based on a crowding distance.

• Type: Conference
• Year: 2007
• Where: IPPS
• Authors: Mario Cámara, Julio Ortega, Francisco de Toro
• Keywords: Distributed And Parallel Computing, IPPS 2007, Multi-objective Optimization, Parallel Evolutionary Procedure, Pareto Front